Evaluating a Consumer LLM for Suicide Risk Response Calibration: A Pilot Study.

Area of Science:

• Artificial Intelligence
• Clinical Psychology
• Digital Health

Background:

• Large language models (LLMs) are increasingly utilized for information seeking and self-guided support.
• Concerns exist regarding LLM safety in high-risk contexts, particularly suicidal ideation.
• Prior research indicates LLMs can detect explicit suicidal language, but their response calibration across risk levels is under-explored.

Purpose of the Study:

• To evaluate the response patterns of Gemini 2.5 Flash across varying suicide risk levels.
• To assess the model's ability to differentiate between non-suicidal distress, suicidal ideation, and imminent suicide risk.
• To identify limitations in LLM detection of implicit suicidal expressions.

Main Methods:

• A pilot study used 60 clinical vignettes from the Self-Directed Violence Classification System (SDVCS).
• Vignettes represented three risk levels: non-suicidal distress (L0), suicidal ideation without plan (L1), and imminent suicide risk (L2).
• LLM outputs were coded for seven response elements: relational support, reflection, psychoeducation, coping skills, action orientation, risk acknowledgment, and crisis resources.

Main Results:

• Relational support and action orientation were common across all risk levels.
• Safety escalation elements increased with risk levels, while psychoeducation declined.
• Coping skills were only present at the no-suicidal risk level, and the model missed implicit suicidality in one vignette.

Conclusions:

• Gemini 2.5 Flash demonstrated an approximation of clinical triage patterns.
• The model exhibited critical limitations in detecting indirect suicidal expressions.
• General-purpose LLMs may aid supervised triage research but are currently unsuitable for autonomous crisis intervention.